Method and apparatus for employing deep learning to infer implementation of regenerative tillage practices

ABSTRACT

A computer-implemented method for predicting a cropland data layer (CDL) for a current year includes: retrieving a first set of records from a historical CDL database, where the first set corresponds to sampled areas of a region taken over a period for a number of years; retrieving a second set of records from a historical imagery database, where the second set corresponds to the sampled areas of the region, the period, and the number of years; employing the second set as inputs to train a deep learning network to generate the first set; retrieving a third set of records from a current imagery database, where the third set corresponds to a prescribed region, and where the third set corresponds to the time period and the current year; and using the third set as inputs and executing the trained deep learning network to generate a predicted CDL for the current year.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to the following co-pending U.S. patentapplications, each of which has a common assignee and common inventors,the entireties of which are herein incorporated by reference.

SER. FILING NO. DATE TITLE           METHOD AND APPARATUS FOR EMPLOYING(CIBO.2014) DEEP LEARNING NEURAL NETWORKTO PREDICT CROPLAND DATA LAYER          METHOD AND APPARATUS FOR EMPLOYING (CIBO.2015) DEEP LEARNINGNEURAL NETWORKTO PREDICT MANAGEMENT ZONES           METHOD AND APPARATUSFOR EMPLOYING (CIBO.2016) DEEP LEARNING NEURAL NETWORKTO INFERREGENERATIVE COVER CROP PRACTICES           METHOD AND APPARATUS FOREMPLOYING (CIBO.2017) DEEP LEARNING TO INFER IMPLEMENTATION OFREGENERATIVE IRRIGATION PRACTICES

BACKGROUND OF THE INVENTION Field of the Invention

This invention relates in general to the field of regenerativeagricultural management practices, and more specifically to methods andsystems for processing imagery data to predict crop-specific land cover,to determine agricultural management zones within fields, and to detectuse of regenerative practices within agricultural management zones.

Description of the Related Art

Climate change is one of the most studied and discussed topics on theplanet, and this level of global concern has sparked numerousinitiatives to reduce Earth's carbon footprint. Initiatives include zerowaste recycling and reuse programs, clean energy programs, conservationmeasures, sustainable transportation programs, and carbon offset andtrading programs. This application focuses on carbon offsets from anagricultural perspective, how they are determined, and how programs togenerate those offsets are monitored and verified.

As one skilled in the art will appreciate, billions of dollars are spenteach year by countries, corporations, small businesses, and individualsto reduce greenhouse gas emissions. But more often than not, the impactof carbon footprint reduction programs is difficult to quantify, mainlybecause such an effort is labor intensive and relies heavily onself-reporting.

Every year the United States Department of Agriculture (USDA) generatesa rasterized, geo-referenced, crop-specific land cover map for thecontinental United States that is known as the Cropland Data Layer(CDL). The CDL is generated from moderate resolution satellite imageryand extensive agricultural ground truth, and is used by all manner ofagricultural-related entities such as universities and private researchfirms, commercial producers, growers, equipment manufacturers,underwriters, real-estate concerns, bankers, conservationists, carbonbrokers, and political entities. The CDL consists of a raster ofcolor-coded pixels, where each pixel comprises a 30 meter by 30 metergeographic area (0.09 hectare pixel resolution), and where each pixel'scolor is indicative of a particular type of “crop.” The crops indicatedrange from conventional cash crops (e.g., corn, cotton, rice) and alsoinclude colors that indicate fallow/idle cropland, wetlands, ice/snow,developed land, forests, pastures, etc., thus mapping land in thecontinental United States to its use from an agricultural perspective.

Albeit extremely useful, generation of the CDL is not timely, for theCDL for a given year is not released to the public until the firstquarter of the following year, which is quite limiting to thoseagricultural entities that require more timely data.

Accordingly, what is needed are methods and systems for predicting acropland data layer at the end of a current year growing season.

What is also needed are methods and apparatus for predicting a croplanddata layer at the end of a current year growing season based solely onsatellite imagery data.

What is further needed are deep learning methods and systems that aretrained on historical CDL data and corresponding satellite imagery datato predict CDL at the end of a current growing season.

What is additionally needed are methods and apparatus that employtransfer learning techniques to detect agricultural management practiceszones within parcels based solely on satellite imagery.

What is finally need are methods and apparatus for inferringimplementation and maintenance of agricultural management practiceswithin determined management practices zones.

SUMMARY OF THE INVENTION

The present invention, among other applications, is directed to solvingthe above-noted problems and addresses other problems, disadvantages,and limitations of the prior art. In one embodiment, acomputer-implemented method for determining agricultural tillagemanagement practices for use within a current growing year is provided,the computer-implemented method including: retrieving a first set ofrecords from a historical cropland data layer database, where the firstset of records corresponds to randomly sampled areas of a firstgeographic region taken over a first time period for a first number ofyears; retrieving a second set of records from a historical imagerydatabase, where the second set of records corresponds to the randomlysampled areas of the first geographic region, the first time period, andthe first number of years; employing the second set of records as inputsto train a first deep learning convolutional neural network to generatethe first set of records and using parameters generated during trainingto configure a trained first deep learning convolutional neural networkfor execution; configuring a second deep learning convolutional neuralnetwork using parameters corresponding to early layers of the trainedfirst deep learning convolutional neural network; retrieving a third setof records and a fourth set of records from an annotated imagerydatabase, where the third set of records includes unannotated imageversions corresponding to a second geographic region, and where thefourth set of records includes annotated image versions corresponding tothe second geographic region, and where the annotated image versionscomprise annotations indicative of management zones, and where the thirdand fourth sets of records correspond to a second time period for asecond number of years; employing the third set of records as inputs totrain upper layers of the second deep learning convolutional neuralnetwork to generate the fourth set of records and using parametersgenerated during training to configure a trained second deep learningconvolutional neural network for execution; retrieving a fifth set ofrecords from a current imagery database, where the fifth set of recordsincludes corresponds to a third geographic region, and where the fifthset of records corresponds to the second time period and the currentgrowing year; using the fifth set of records as inputs and executing thetrained second deep learning convolutional neural network to generatepredicted agricultural management zones for the current growing year;and aggregating the fifth set of record into residue indices for parcelswithin the third geographic region, and processing the residue indicesover the second time period for the current growing year to infertillage practices for each of the predicted agricultural managementzones as demarcated by boundaries of each of the parcels.

One aspect of the present invention comprehends a computer-readablestorage medium storing instructions that, when executed by a computer,cause the computer to perform a method for determining agriculturaltillage management practices for use within a current growing year, themethod including: retrieving a first set of records from a historicalcropland data layer database, where the first set of records correspondsto randomly sampled areas of a first geographic region taken over afirst time period for a first number of years; retrieving a second setof records from a historical imagery database, where the second set ofrecords corresponds to the randomly sampled areas of the firstgeographic region, the first time period, and the first number of years;employing the second set of records as inputs to train a first deeplearning convolutional neural network to generate the first set ofrecords and using parameters generated during training to configure atrained first deep learning convolutional neural network for execution;configuring a second deep learning convolutional neural network usingparameters corresponding to early layers of the trained first deeplearning convolutional neural network; retrieving a third set of recordsand a fourth set of records from an annotated imagery database, wherethe third set of records includes unannotated image versionscorresponding to a second geographic region, and where the fourth set ofrecords includes annotated image versions corresponding to the secondgeographic region, and where the annotated image versions compriseannotations indicative of management zones, and where the third andfourth sets of records correspond to a second time period for a secondnumber of years; employing the third set of records as inputs to trainupper layers of the second deep learning convolutional neural network togenerate the fourth set of records and using parameters generated duringtraining to configure a trained second deep learning convolutionalneural network for execution; retrieving a fifth set of records from acurrent imagery database, where the fifth set of records includescorresponds to a third geographic region, and where the fifth set ofrecords corresponds to the second time period and the current growingyear; using the fifth set of records as inputs and executing the trainedsecond deep learning convolutional neural network to generate predictedagricultural management zones for the current growing year; andaggregating the fifth set of record into residue indices for parcelswithin the third geographic region, and processing the residue indicesover the second time period for the current growing year to infertillage practices for each of the predicted agricultural managementzones as demarcated by boundaries of each of the parcels.

Another aspect of the present invention envisages a computer programproduct for determining agricultural tillage management practices foruse within a current growing year, the computer program productincluding: a computer readable non-transitory medium having computerreadable program code stored thereon, the computer readable program codeincluding: program instructions to retrieve a first set of records froma historical cropland data layer database, where the first set ofrecords corresponds to randomly sampled areas of a first geographicregion taken over a first time period for a first number of years;program instructions to retrieve a second set of records from ahistorical imagery database, where the second set of records correspondsto the randomly sampled areas of the first geographic region, the firsttime period, and the first number of years; program instructions toemploy the second set of records as inputs to train a first deeplearning convolutional neural network to generate the first set ofrecords and to use parameters generated during training to configure atrained first deep learning convolutional neural network for execution;program instructions to configure a second deep learning convolutionalneural network using parameters corresponding to early layers of thetrained first deep learning convolutional neural network; programinstructions to retrieve a third set of records and a fourth set ofrecords from an annotated imagery database, where the third set ofrecords includes unannotated image versions corresponding to a secondgeographic region, and where the fourth set of records includesannotated image versions corresponding to the second geographic region,and where the annotated image versions comprise annotations indicativeof management zones, and where the third and fourth sets of recordscorrespond to a second time period for a second number of years; programinstructions to employ the third set of records as inputs to train upperlayers of the second deep learning convolutional neural network togenerate the fourth set of records and to use parameters generatedduring training to configure a trained second deep learningconvolutional neural network for execution; program instructions to usethe fifth set of records as inputs and to execute the trained seconddeep learning convolutional neural network to generate predictedagricultural management zones for the current growing year; and programinstructions to aggregate the fifth set of record into residue indicesfor parcels within the third geographic region, and processing theresidue indices over the second time period for the current growing yearto infer tillage practices for each of the predicted agriculturalmanagement zones as demarcated by boundaries of each of the parcels.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features, and advantages of the presentinvention will become better understood with regard to the followingdescription, and accompanying drawings where:

FIG. 1 is a block diagram illustrating how users presently access datafrom the USDA Cropland Data Layer database;

FIG. 2 is a block diagram depicting a cropland data prediction systemaccording to the present invention;

FIG. 3 is a block diagram featuring a cropland data prediction serveraccording to the present invention, such as may be employed within thesystem of FIG. 2 ;

FIG. 4 is a flow diagram showing a method according to the presentinvention for training a deep cropland data layer model, such as may beperformed by the system of FIG. 2 ;

FIG. 5 is a flow diagram illustrating a method according to the presentinvention for executing a deep cropland data layer model, such as may beperformed by the system of FIG. 2 ;

FIG. 6 is a flow diagram detailing a method according to the presentinvention for training a deep management zones model, such as may beperformed by the system of FIG. 2 ;

FIG. 7 is a flow diagram depicting a method according to the presentinvention for executing a deep management zones model, such as may beperformed by the system of FIG. 2 ; and

FIG. 8 is a flow diagram featuring a method according to the presentinvention for inferring implementation of agricultural managementpractices within parcel management zones.

DETAILED DESCRIPTION

Exemplary and illustrative embodiments of the invention are describedbelow. It should be understood at the outset that although exemplaryembodiments are illustrated in the figures and described below, theprinciples of the present disclosure may be implemented using any numberof techniques, whether currently known or not. In the interest ofclarity, not all features of an actual implementation are described inthis specification, for those skilled in the art will appreciate that inthe development of any such actual embodiment, numerous implementationspecific decisions are made to achieve specific goals, such ascompliance with system-related and business-related constraints, whichvary from one implementation to another. Furthermore, it will beappreciated that such a development effort might be complex andtime-consuming, but would nevertheless be a routine undertaking forthose of ordinary skill in the art having the benefit of thisdisclosure. Various modifications to the preferred embodiment will beapparent to those skilled in the art, and the general principles definedherein may be applied to other embodiments. Therefore, the presentinvention is not intended to be limited to the particular embodimentsshown and described herein, but is to be accorded the widest scopeconsistent with the principles and novel features herein disclosed.

The present invention will now be described with reference to theattached figures. Various structures, systems, and devices areschematically depicted in the drawings for purposes of explanation onlyand so as to not obscure the present invention with details that arewell known to those skilled in the art. Nevertheless, the attacheddrawings are included to describe and explain illustrative examples ofthe present invention. Unless otherwise specifically noted, articlesdepicted in the drawings are not necessarily drawn to scale.

The words and phrases used herein should be understood and interpretedto have a meaning consistent with the understanding of those words andphrases by those skilled in the relevant art. No special definition of aterm or phrase (i.e., a definition that is different from the ordinaryand customary meaning as understood by those skilled in the art) isintended to be implied by consistent usage of the term or phrase herein.To the extent that a term or phrase is intended to have a specialmeaning (i.e., a meaning other than that understood by skilled artisans)such a special definition will be expressly set forth in thespecification in a definitional manner that directly and unequivocallyprovides the special definition for the term or phrase. As used in thisdisclosure, “each” refers to each member of a set, each member of asubset, each member of a group, each member of a portion, each member ofa part, etc.

Applicants note that unless the words “means for” or “step for” areexplicitly used in a particular claim, it is not intended that any ofthe appended claims or claim elements are recited in such a manner as toinvoke 35 U.S.C. § 112(f).

Definitions

Central Processing Unit (CPU): The electronic circuits (i.e.,“hardware”) that execute the instructions of a computer program (alsoknown as a “computer application,” “application,” “application program,”“app,” “computer program,” or “program”) by performing operations ondata, where the operations may include arithmetic operations, logicaloperations, or input/output operations. A CPU may also be referred to asa “processor.”

Module: As used herein, the term “module” may refer to, be part of, orinclude an application specific integrated circuit (ASIC), an electroniccircuit, a processor (shared, dedicated, or group) and/or memory(shared, dedicated, or group) that execute one or more computerprograms, a combinational logic circuit, and/or other suitablecomponents that provide the described functionality.

Microprocessor: An electronic device that functions as a CPU on a singleintegrated circuit. A microprocessor receives digital data as input,processes the data according to instructions fetched from a memory(either on-die or off-die), and generates results of operationsprescribed by the instructions as output. A general-purposemicroprocessor may be employed in a desktop, mobile, or tablet computer,and is employed for uses such as computation, text editing, multimediadisplay, and Internet browsing. A microprocessor may also be disposed inan embedded system to control a wide variety of devices includingappliances, mobile telephones, smart phones, and industrial controldevices.

Multi-Core Processor: Also known as a multi-core microprocessor, amulti-core processor is a microprocessor having multiple CPUs (“cores”)fabricated on a single integrated circuit.

Instruction Set Architecture (ISA) or Instruction Set: A part of acomputer architecture related to programming that includes data types,instructions, registers, addressing modes, memory architecture,interrupt and exception handling, and input/output. An ISA includes aspecification of the set of opcodes (i.e., machine languageinstructions), and the native commands implemented by a particular CPU.

x86-Compatible Microprocessor: A microprocessor capable of executingcomputer applications that are programmed according to the x86 ISA.

Microcode: A term employed to refer to a plurality of microinstructions. A micro instruction (also referred to as a “nativeinstruction”) is an instruction at the level that a microprocessorsub-unit executes. Exemplary sub-units include integer units, floatingpoint units, MMX units, and load/store units. For example, microinstructions are directly executed by a reduced instruction set computer(RISC) microprocessor. For a complex instruction set computer (CISC)microprocessor such as an x86-compatible microprocessor, x86instructions are translated into associated micro instructions, and theassociated micro instructions are directly executed by a sub-unit orsub-units within the CISC microprocessor.

Internet: The Internet is a global wide area network connectingcomputers throughout the world via a plurality of high-bandwidth datalinks which are collectively known as the Internet backbone. TheInternet backbone may be coupled to Internet hubs that route data toother locations, such as web servers and Internet Service Providers(ISPs). The ISPs route data between individual computers and theInternet and may employ a variety of links to couple to the individualcomputers including, but not limited to, cable, DSL, fiber, and Wi-Fi toenable the individual computers to transmit and receive data over in theform of email, web page services, social media, etc. The Internet mayalso be referred to as the world-wide web or merely the web.

In view of the above background discussion on present-day techniques forgenerating crop statistics and presenting these statistics for publicconsumption, a discussion of the disadvantages and limitations of thesetechniques will be provided with reference to FIG. 1 . Following this, adiscussion of the present invention will be provided with reference toFIGS. 2-8 . The present invention overcomes the problems associated withpresent-day crop statistics generation and distribution techniques byproviding methods and systems for early and independent prediction ofcrop statistics such as cash crop and cover crop types, planting andharvesting dates, management zones within fields, and detection ofregenerative management practices.

Referring to FIG. 1 , a block diagram 100 is presented illustrating howusers presently access data from the USDA Cropland Data Layer database.As one skilled in the art will appreciate, every year the United StatesDepartment of Agriculture (USDA) National Agricultural StatisticsService (NASS) produces the USDA Cropland Data Layer (CDL), which is arasterized, geo-referenced, crop-specific land cover map for thecontinental United States. According to the USDA, CDL data is “createdannually using moderate resolution satellite imagery and extensiveagricultural ground truth.” Boundary layers are provided that allowusers to layover boundaries of counties, agricultural statisticsdistricts (ASDs), states, regions, lakes and rivers, highways, and cropfrequencies. CDLs date back to 1997 and the most current CDL is for2020. Users are allowed to select geographic areas of interest, and viewacreage statistics for a specific year or view the change from one yearto another. Data from the CDL can be exported and may be used forresearch and analysis in a vast number of disciplines related toagricultural. Users include scientists, commercial producers, growers,equipment manufacturers, underwriters, real-estate concerns, bankers,conservationists, carbon brokers, and political entities. The CDLconsists of a raster of color-coded pixels, where each pixel comprises a30 meter by 30 meter geographic area (0.09 hectare pixel resolution),and where each pixel's color is indicative of a particular type of“crop.” The crops indicated range from conventional cash crops (e.g.,corn, cotton, rice) and also include colors that indicate fallow/idlecropland, wetlands, ice/snow, developed land, forests, pastures, etc.,thus mapping land in the continental United States to its use from anagricultural perspective. There are over 100 crops indicated. The CDL ishosted by the CropScape web server and is free for use by the public.Further information on the CDL may be found at the following USDA URL:https://data.nal.usda.gov/dataset/cropscape-cropland-data-layer.

Accordingly, the diagram 100 shows the USDA CropScape web server 101that provides access to a historical cropland data layer database 102that includes the above-noted CDL for the years 1997-2020. The presentinventors note that the database 102 is referred to as “historical”because, as one skilled in the art will appreciate, the data provided inthe CDL does not include the current year's data. That is, data for aprevious year (e.g., 2020) is not published until late January or earlyFebruary of the current year (e.g., 2021). The web server 101 is coupledto the internet 110, which allows users to search and access the CDLfrom a number of different devices that include, but are not limited to,desktop/laptop computers 121, tablet computers 123, and smartphones 125.

In operation, a user of one of the devices 121, 123, 125 may access theCropScape web server 101, and select from a number of parameters todownload through the internet rasterized images 103 meeting criteria inaccordance with input geographic area of interest along with theabove-noted layering features. The rasterized images 103 are configuredby the server 101 to comport with display capabilities of the user'sdevices 121, 123, 125. As is show in the diagram 100, a desktop/laptopCDL image 122 is displayed on the desktop/laptop computer 121, atablet-sized CDL image 124 is displayed on the tablet computer 123, anda smartphone-sized CDL image 126 is displayed on the smartphone 125.

While exceedingly beneficial for many classes of users, the presentinventors have observed that the present-day mechanisms for providingthe CDL are limited, primarily because of the time lag that is requiredfor the USDA NASS to generate and release the CDL for the previous year.Notwithstanding that previous year's CDLs are useful for manyapplications, there are a significant number of applications thatrequire CDL data that is current. For instance, consider a carbon brokerthat has engaged a grower to implement certain regenerative managementpractices during the growing season. As one skilled in the art willappreciate, such regenerative practices include, but are not limited to,cover cropping, crop rotation, low/no irrigation, low/no tillage,composting, managed grazing, organic fertilization, etc. The growercommits to implement one or more of the regenerative managementpractices, the effects of which the carbon broker may quantify in termsof reduction of the grower's carbon footprint. This quantification istypically expressed in carbon credit units, which are then sold by thebroker to individuals and businesses that are motivated to reducegreenhouse emissions. What is important to the carbon broker is todetermine, monitor, and verify that the grower actually implemented themanagement practices that have been engaged. Incentive payments by thebroker to the grower, whether pre-implementation or post-implementation,rest squarely upon independent determination, monitoring, andverification.

To monitor a single regenerative practices contract can be easilyperformed manually by dispatching representatives of the broker to thegrower's fields to observe whether the practices were implemented ornot, but such manual techniques cannot be economically scaled to addresstens of thousands of contracts to corresponding growers. Accordingly,one aspect of scalable, automated regenerative practices implementationdetermination, monitoring, and verification requires CDL data that iscurrent. More specifically, if a grower commits to implement, say, notillage, then it is important to verify at the end of the growing season(typically) that the grower's fields have not been tilled.

The present inventors have also observed a pull in the art from thescientific and research community to obtain current predictions of CDLthat are not subject to the latencies exhibited by government agencies.Accordingly, the present invention will now be discussed with referenceto FIGS. 2-8 .

Turning to FIG. 2 , a block diagram is presented depicting a croplanddata prediction system 200 according to the present invention. Thecropland data prediction system 200 may include a cropland dataprediction server 220 that is coupled to the internet 210. The server220 is also coupled to the USDA CDL database 201 via the CropScape webserver discussed above with reference to FIG. 1 . The server 220 is alsocoupled to public and commercial databases 202-205 that include one ormore imagery databases 202, one or more imagery annotation databases203, one or more weather databases 204, and one or more agriculturalmanagement practices databases 205. Though shown in FIG. 2 as beingdirectly coupled to the cropland data prediction server 220, preferablythe databases 201-205 are accessed via the internet 210. Data accessedfrom the one or more imagery databases 202 may comprise aerial andsatellite imagery of agricultural parcels in the United Statesconsisting of optical, near infrared, and short-wave infrared bands thatare employed to generated well known vegetative and tillage indices.Preferably, the satellite imagery data comprises Sentinel and/or Landsatimagery comprising the following bands used to generate EnhancedNormalized Vegetative Indices (eNVDIs) and Normalized Difference TillageIndices (NTDIs): red channels, green channels, blue channels, nearInfrared channels, cloud mask channels, and shortwave infrared channels.In one embodiment, these channels are obtained from Sentinel satelliteoverflights.

Data accessed from the one or more imagery annotation databases 203 mayinclude both unlabeled (“unannotated”) and manually labeled(“annotated”) videos of different geographic areas, where eachunannotated and corresponding annotated video comprises a plurality ofimages of a corresponding geographic area in the bands noted above thatare taken over a period of time. The labeled videos are manuallyannotated by analysts trained to detect one or more distinctagricultural management zones within the corresponding geographic area.For example, pixels within the plurality of images may be labeled aseither part of a management zone or not part of a management zone, asdetermined by the analysts. Management zones are visually distinguishedby the analysts as exhibiting different agricultural managementpractices characteristics such as different crops, different growthrates and patterns, different planting and harvesting dates, etc. Toprovide coverage for the entire continental United States, the presentinvention contemplates videos covering approximately one percent of thetotal geographic area reasonably dispersed so as to account for regionaldifferences. For example, to provide coverage for the corn belt(approximately ⅓ of the continental United States), roughly 20,000annotated videos are required. Fewer annotated videos are required forcoverage of the cotton belt, the wheat belt, and remaining growingregions.

Data from the one or more weather databases 204 may comprise historicalweather records up to the present associated with geographic areas thatinclude, but are not limited to, temperature, humidity, rainfall,snowfall, wind, and natural disasters (e.g., flood, tornados, etc.).

Data from the one or more management practices databases 205 maycomprise grower-reported historical management practices as discussedabove, and commitments to implementation of management practices for thecurrent season. The data may additionally comprise grower managementpractices that are common to different geographical areas. The data mayfurther comprise scenarios of “typical farming” on for fields in a givenlocation. For example, given a field in central Illinois, the data maycomprise indicators that farmers in this area typically plant a corn/soyrotation and maturity group for corn is typically 110 RM, which would beplanted around May 15, and that farmers typically apply 150 pounds ofnitrogen fertilizer in a split application: 100 pounds the day beforeplanting, and 50 pounds as a mid-season side-dressing, and that thematurity group for soy is 3.8, which would be planted around May 2,requiring no fertilizer. The data may further provide indicators thatmost farmers in this area use conventional tillage, but 10% of them useconservation tillage.

The cropland data prediction server 220 may comprise a web servicesprocessor 231 that is coupled to a parcel database 221. The croplanddata prediction server 220 may additionally comprise a cropland datalayer (CDL) prediction processor 232, a database management processor233, a management zones prediction processor 234, a crop inferenceprocessor 235, an irrigation inference processor 237, a tillageinference processor 237 and a remote sensing processor 238, all of whichare coupled together, and which are coupled to the parcel database 221.The CDL prediction processor 232 is coupled to a predicted CDL database222. The management zones prediction processor 234 is coupled to apredicted management zones database 223.

Operationally, the cropland data prediction server 220 is configured toprovide current predictions of the USDA CDL for the continental UnitedStates at the end of the current year growing season, preferably in themonth of October, though other embodiments are contemplated.Accordingly, predictions of the USDA CDL are generated roughly three tofour months prior to release of the CDL by the USDA in the followingyear. As will be described in further detail below, the currentpredictions are based upon historical CDL data obtained from theCropScape database in conjunction with historical and current-yearimagery data accessed from the one or more imagery databases 202. CDLpredictions generated by the CDL prediction processor 232 are stored inthe predicted CDL database 222.

The management zones prediction processor 234 is configured to providecurrent predictions of management zones within each geographical area inthe continental United States. The management zones predictions arebased upon parameters generated by the CDL prediction processor 232 anddata provided by the one or more annotated imagery databases 203.Management zones predictions generated by the management zonespredictions processor 234 are stored in the predicted management zonesdatabase 223.

The crop inference processor 235 is configured to generate inferencesrelated to crops planted in the management zones that include, but arenot limited to, primary (“cash”) crop type along with planting andharvesting dates. For fields comprising multiple management zones,differing crop types and dates may be inferred as indicative toregenerative management practices such as crop rotation and covercropping. The crop inferences are based upon current imagery data fromthe one or more imagery databases 202, current predicted CDL data,current predicted management zones data, weather data from the one ormore weather databases 204, and management practices data (if available)from the one or more management practices databases 205.

The irrigation inference processor 236 is configured to generateinferences related to irrigation practices within the management zonessuch as conventional irrigation, low irrigation, and no irrigation. Theirrigation inferences are based upon current imagery data from the oneor more imagery databases 202, current predicted CDL data, currentpredicted management zones data, weather data from the one or moreweather databases 204, and management practices data (if available) fromthe one or more management practices databases 205.

The tillage inference processor 237 is configured to generate inferencesrelated to tillage practices within the management zones such asconventional tillage, conservation tillage, and no tillage. The tillageinferences are based upon current imagery data from the one or moreimagery databases 202, current predicted CDL data, current predictedmanagement zones data, weather data from the one or more weatherdatabases 204, and management practices data (if available) from the oneor more management practices databases 205.

The remote sensing processor 238 is configured to access, cleanse, andformat imagery data obtained from the one or more imagery databases 202for use by the CDL prediction processor 232, the management zonesprediction processor 234, the crop inference processor 235, andirrigation inference processor 236, and the tillage inference processor.

The database management processor 233 is configured to manage storage ofCDL predictions, management zones predictions, crop inferences,irrigation inferences, and tillage inferences in the parcel database 221and to correlate and associate those predictions and inferences withspecific fields and owners in the continental United States. As is notedabove, boundary layer options provided by the UDSA Cropland Data Layerare county, agricultural statistics districts, state, and region.Advantageously, the parcel database 221 comprises public records for allagricultural parcels in the continental United States that allow for anadditional parcel-level (“field-level”) boundary layover of predictedCDL. Advantageously, CDL and management zones predictions generated bythe server 220 based on satellite imagery may be used to identify cropsat the field level. In addition, the crop, irrigation, and tillageinferences may be applied at the field level as well, and may be thusemployed to determine, monitor, and verify grower's implementation ofregenerative management practices.

The web services processor 231 is configured to accepts queries fromusers via the internet 210 and to format and transmit results of thosequeries from the parcel database 221.

The cropland data prediction server 220 according to the presentinvention is configured to perform the functions and operations asdiscussed above. The server 220 may comprise digital and/or analoglogic, circuits, devices, or microcode (i.e., micro instructions ornative instructions), or a combination of logic, circuits, devices, ormicrocode, or equivalent elements that are employed to execute thefunctions and operations according to the present invention as noted.The elements employed to accomplish these operations and functionswithin the server 220 may be shared with other circuits, microcode,etc., that are employed to perform other functions and/or operationswithin the server 220. According to the scope of the presentapplication, microcode is a term employed to refer to a plurality ofmicro instructions. A micro instruction (also referred to as a nativeinstruction) is an instruction at the level that a unit executes. Forexample, micro instructions are directly executed by a reducedinstruction set computer (RISC) microprocessor. For a complexinstruction set computer (CISC) microprocessor such as an x86-compatiblemicroprocessor, x86 instructions are translated into associated microinstructions, and the associated micro instructions are directlyexecuted by a unit or units within the CISC microprocessor.

The server 220 according to the present invention may additionallycomprise one or more application programs executing thereon to performthe operations and functions described above, and which will bedisclosed in further detail with reference to FIG. 3 .

Now referring to FIG. 3 , a block diagram is presented featuring acropland data prediction server 300 according to the present invention,such as may be employed within the system 200 of FIG. 2 . The server 300may include one or more central processing units (CPU) 301 that arecoupled to memory 306 having both transitory and non-transitory memorycomponents therein. The CPU 301 is also coupled to a communicationscircuit COMMS 302 that couples the SERVER 300 to the internet 210 viaone or more wired and/or wireless links 303. The links 303 may include,but are not limited to, Ethernet, cable, fiber optic, and digitalsubscriber line (DSL). As part of the network path to and through theinternet 210, internet service providers (ISPs) may employ wirelesstechnologies from point to point as well.

The server 300 may also comprise input/output I/O circuits 304 thatinclude, but are not limited to, data entry and display devices (e.g.,keyboards, monitors, touchpads, etc.). The memory 306 may be coupled toa parcel database 221 and to the databases 201-205 described withreference to FIG. 2 above. Though the cropland data prediction server200 of FIG. 2 is shown directly coupled to databases 201-205, thepresent inventors note that interfaces to these data sources mayexclusively be through the communications circuit 302 or may be througha combination of direct interface and through the communications circuit302, according to the source of data. In addition, Though the croplanddata prediction server 200 of FIG. 2 is shown to additionally includethe predicted CDL database 222 and the predicted management zonesdatabase 223, interfaces to these data stores may exclusively beaccomplished through the communications circuit 302 or may be through acombination of direct interface and through the communications circuit302, according to the type of data stores.

The memory 306 may include an operating system 307 such as, but notlimited to, Microsoft Windows, Mac OS, Unix, and Linux, where theoperating system 307 is configured to manage execution by the CPU 1001of program instructions that are components of one or more applicationprograms. In one embodiment, a single application program comprises aplurality of code segments 331-338 resident in the memory 306 and whichare identified as a web services code segment 331, a CDL prediction codesegment 332, a database management code segment 333, a management zonesprediction code segment 334, a crop inference code segment 335, anirrigation inference code segment 336, a tillage inference code segment337 and a remote sensing code segment 338.

Operationally, the cropland data prediction server 300 may execute oneor more of the code segments 331-337 under control of the OS 307 asrequired to enable the server 300 to ingest data from external datasources 201-205 and to employ the data from the sources 201-205 topredict the cropland data layer at the end of a growing season,typically in October of the same year, to determine agriculturalmanagement zones within the predicted cropland data layer, and to inferimplementation and maintenance of regenerative agricultural managementpractices corresponding to one or more parcels having correspondingidentification data that is stored in the parcel database 221. One ormore of the code segments 331-337 may be executed to update thepredicted CDL database 222, the parcel database 221, and the predictedmanagement zones database 223, with corresponding results generated bythe CDL prediction code segment 332, the management zones predictioncode segment 334, the crop inference code segment 335, the irrigationinference code segment 336, and the tillage inference code segment 337.The web services code segment 231 many execute to access data requestedby users via COMMS 302 from the parcel database 221, and may format andtransmit results of user's queries via COMMS 302 over one or more of thelinks 303. One embodiment of the present invention contemplates datastored in the parcel database 221 for approximately 20 millionagricultural parcels within the continental United States, where thedata may be rapidly and easily searched and accessed.

The database management code segment 333 may be executed to store datato or retrieve data from the parcel database 221 resulting from CDLpredictions made by the CDL prediction code segment 332, management zonepredictions made by the management zones prediction code segment 334,crop inferences made by the crop inference code segment 335, irrigationinferences made by the irrigation inference code segment 336, andtillage inferences made by the tillage inference code segment 337. Thedatabase management code segment 333 may further execute to assist theweb services code segment 331 in providing query results from the parceldatabase 221 to users via COMMS 302. The database management codesegment 333 may additionally execute to provide storage within theparcel database 221 of imagery data as described above that isassociated with one or more parcels. The database management codesegment 333 may also execute to provide storage within the parceldatabase 221 of annotated imagery data as described above that isassociated with one or more parcels. The database management codesegment 333 may further execute to provide storage within the parceldatabase 221 of weather data and management practices data as describedabove that is associated with one or more parcels.

The CDL prediction code segment 332 may execute to perform the functionsdescribed above to generate predicted cropland data layers for thecontinental United States at the end of a current year growing season,in one embodiment, where predictions made by the CDL prediction codesegment 332 are based, as will be described in further detail below, onhistorical USDA CDL data, geographically corresponding imagery data, andimagery data from the current year growing season. In anotherembodiment, the CDL prediction code segment 332 may execute to performthe functions described above to generate predicted cropland data layersfor the continental United States during a current year growing season.

The management zones prediction code segment 334 may execute to performthe functions described above to generate predicted management zones forthe continental United States at the end of a current year growingseason, in one embodiment, where predictions made by the managementzones prediction code segment 334 are based, as will be described infurther detail below, on early level deep learning model parametersprovided by the CDL prediction code segment 332, annotated imagery data,and imagery data from the current year growing season. In anotherembodiment, the management zones prediction code segment 334 may executeto perform the functions described above to generate predictedmanagement zones within the predicted cropland data layers generated bythe CDL prediction code segment 332 for the continental United Statesduring a current year growing season.

The crop inference code segment 335 may execute to perform the functionsdescribed above to generate inferences for parcels stored within theparcel database 221 and management zones within those parcels (asdetermined by the management zones prediction code segment 334) thatinclude, but are not limited to, primary crop type, planting dates, andharvesting dates; secondary crop type, planting dates, and harvestingdates; and like inferences for additional management zones (ifidentified) within the parcels. The crop inference code segment 335 mayutilize, if available, management practices provided by growers or otherground truth sources, CDL predictions, management zones predictions,weather data, and current imagery data to generate the above notedinferences.

The irrigation inference code segment 336 may execute to perform thefunctions described above to generate inferences for parcels storedwithin the parcel database 221 and management zones within those parcels(as determined by the management zones prediction code segment 334) thatinclude, but are not limited to, irrigation practices (conventionalirrigation, low irrigation, no irrigation) along with irrigation dates.The irrigation inference code segment 336 may utilize, if available,management practices provided by growers or other ground truth sources,CDL predictions, management zones predictions, weather data, and currentimagery data to generate the above noted inferences.

The tillage inference code segment 336 may execute to perform thefunctions described above to generate inferences for parcels storedwithin the parcel database 221 and management zones within those parcels(as determined by the management zones prediction code segment 334) thatinclude, but are not limited to, tillage practices (conventionaltillage, conservation tillage, no tillage) along with tillage dates. Thetillage inference code segment 336 may utilize, if available, managementpractices provided by growers or other ground truth sources, CDLpredictions, management zones predictions, weather data, and currentimagery data to generate the above noted inferences.

The remote sensing code segment 338 may execute to perform the functionsdescribed above to retrieve historical and current imagery data, tocleanse the imagery data prior to employment by code segments 332-337,and to format the imagery data for employment by code segments 332-337.

Now turning to FIG. 4 , a flow diagram 400 is presented showing a methodaccording to the present invention for training a deep cropland datalayer model, such as may be performed by the system 200 of FIG. 2 . Thedeep cropland data layer model is generated by the CDL predictionprocessor 232 and is employed by the CDL prediction processor 232 togenerate a predicted cropland data layer at the end of a current yeargrowing season, prior to when the USDA releases the CropScape CDL data.In one embodiment, the deep cropland data layer model is a deep learningconvolutional neural network comprising 5 layers that is trained usinghistorical imagery data to predict corresponding historical CDL. Oncetrained, the deep cropland data layer model may be executed usingparameters derived from the training along with current year imagerydata to generate predicted CDL for the continental United States. In oneembodiment, the deep cropland data layer model employs 128×128 pixelhistorical imagery tiles, where each pixel corresponds to 10-meterground resolution for Sentinel images. The present inventors note thatthe 128×128 pixel historical imagery tiles may, in some cases, beconfused with Sentinel the granules, also called “tiles,” which are 100km×100 km in size; however, the clarity purposes, “tiles” will beemployed herein to connote 128×128 pixel images. In one embodiment, eachtile comprises five optical and infrared bands (i.e., red, green, blue,near infrared, and cloud mask). Another embodiment contemplates sixoptical, infrared, and short-wave infrared bands (i.e., red, green,blue, near infrared, short-wave infrared, and cloud mask). In oneembodiment, two tiles are generated per month, equally spaced in time,for the months of May through October, thus providing 12 tiles per yearfor both training and execution. Accordingly, each tile comprises a1,280 meter by 1,280 meter geographic region and these tiles are used astraining inputs, as will be described below, for generation of acorresponding geographic area of historical CDL. As is noted above,since CDL has 30 meter ground resolution, pixels of the CDL arereplicated times 3 to comport with the Sentinel imagery tiles. Thus,twelve 128×128 satellite imagery tiles for a given year are used asinputs to train the deep cropland data layer model to predictcorresponding formatted 128×128 CDL tiles for the same year. In oneembodiment, the previous three years of CDL and imagery data are used totrain the model.

In one embodiment, in a random sample step 406, random geographic areasamples of historical satellite imagery data are accessed from thehistorical imagery database 404 via bus HI and random sample locationsare provided to the historical CDL database 402 via bus SAM forretrieval of historical CDL that corresponds (in both growing year andgeographic location) to the random geographic area samples of thehistorical satellite imagery data. Corresponding historical CDL samplesare provided via bus SHC. Random sampling is employed to train the modelbecause it is trained against ground truth data (i.e., USDA CDL).Historical satellite imagery sample tiles are provided via bus SHI toblock 408 where the tiles are cleansed and formatted. Cleansing andformatting may comprise removal of duplicate information, inferringmissing values, substituting for unconventional characters and symbols,removal of outlier values, and inference of missing values. Cleansed andformatted historical satellite imagery tiles are provide via bus FSHIalong with corresponding historical CDL sample tiles via bus SHC to thestep 410, where parameters (e.g., weights) of the deep CDL predictionmodel are iteratively trained. In one embodiment 180,000 geographic areasamples of historical satellite imagery and corresponding historical CDLare employed, where each of three years comprises 60,000 sample tiles.In one embodiment, 40,000 of the 60,000 tiles in each year are employedas training data and the remaining 20,000 tiles are employed as avalidation set to evaluate model accuracy and performance. Thehistorical satellite imagery and corresponding CDL tiles are provided tothe deep CDL model in random order. Backward propagation of errors (alsoknown as backpropagation) is employed to train the convolutional deeplearning network. As one skilled in the art will appreciate,backpropagation is technique for feedback of total loss into a deeplearning convolutional neural network to determine how much of the lossevery node is responsible for, and for subsequently updating modelweights in a manner that minimizes the loss by giving the nodes withhigher error rates lower weights and vice versa. Once the deep learningCDL prediction model is trained at step 410, parameters for the 12-layerdeep learning model are provided via bus DCP and stored in a deep CDLprediction model database 412. The present invention contemplatesretraining the deep learning CDL prediction model every year using newlyreleased USDA CDL and corresponding newly acquired satellite imagery,thus improving the accuracy of predicted CDL. The present inventors notethat the deep CDL prediction model according to the present invention istrained to predict CDL, which is indicative of land cover type.

Referring now to FIG. 5 , a flow diagram 500 is presented illustrating amethod according to the present invention for executing a deep croplanddata layer model, such as may be performed by the system 200 of FIG. 2 .Once trained in accordance with the steps discussed above with referenceto FIG. 4 , the deep CDL prediction model is configured using the modelparameters derived during training. These model parameters are providedfrom database 512 on bus DCP.

Current year imagery data for the growing season is accessed fromdatabase 504 on bus CI and is cleansed and formatted in step 508 in thesame manner as is discussed above with reference to step 408 of FIG. 4 .Preferably, the current year imagery data is of the same format(128×128) as used for training and comprises 12 tiles that aredistributed approximately even in time over a 6-month growing seasonfrom May through October. While the deep CDL prediction model is trainedto predict CDL for the entire continental United States, the presentinventors note that selected geographical areas may be addressed throughexecution of the model, where the areas are demarcated by the imagerydata that is provided via bus FCI.

The deep CDL prediction model is sequentially executed at step 510 togenerate predicted CDL that corresponds to each of the image sequencesprovided via bus FCI. The predicted CDL, as demarcated by the outergeographic boundaries of the image sequences, is provided via bus PCDLand is stored in database 522. It is noted that geographic range ofpredicted CDL that is generated by the deep CDL prediction model at step510 is a function of the geographic range of current imagery dataprovided to the model via bus FCI. For example, a user may be interestedin CDL for a state (e.g., Kentucky) or a growing region (e.g., the cornbelt). Accordingly, only current imagery covering the desired geographicarea is retrieved from database 504. The predicted CDL is a USDACDL-equivalent raster in both pixel resolution and color coding. Sinceimagery tiles and corresponding predicted CDL are only associated withlongitude and latitude, the present invention contemplates stitchingadjacent CDL tiles together using coordinates of farms and parcels asstored in the parcel database 221 to provide for a field-level layoverof CDL. This aggregation of CDL predictions at the field level isperformed by the database management processor 233.

Turning now to FIG. 6 , a flow diagram 600 is presented detailing amethod according to the present invention for training a deep managementzones prediction model, such as may be performed by the system 200 ofFIG. 2 . The deep management zones model is employed to determine one ormore management zones within 128×128 pixel imagery tiles based solely onthe imagery itself. As noted above management zones are anything that atrained analyst would visually observe from a time sequence of imagerytiles as exhibiting different agricultural management practices thanother portions of the imagery tiles. Such observable managementpractices include, but are not limited to, crop type, specific cultivaror crop variety, planting and harvesting data, planting density (e.g.,row spacing), tillage types and dates, fertilizer application, croprotation and cover cropping, irrigation (e.g., dates and amounts),buffer zoning, and drainage control. The analysts may not specificallyknow which management practice is being applied; what is known is thatimage pixels within an area demarcated by a polygon within an image tileutilizes a different management practice than other areas demarcated byother polygons within the image tile. Preferably, pixels within the128×128 imagery tiles are labeled to indicate one or more polygonswithin the image as management zones. Geographic areas (1,280meters×1,280 meters) corresponding to individual imagery tiles maycomprise one or more management zones.

The present inventors have observed that detection of agriculturalmanagement zones from imagery data is very similar to early layerfeature determination that is employed by the deep CDL prediction modelfrom imagery data, thus providing an excellent case for so-calledtransfer learning. Accordingly, the deep management zones predictionmodel according to the present invention, like the deep learning CDLpredication model discussed above with reference to FIGS. 4-5 , is adeep learning convolutional neural network comprising 5 layers, in oneembodiment, that is trained using historical unlabeled and labeledimagery data to predict one or more management zones within a timesequence of imagery tiles. Preferably, parameters corresponding to theearly layers of the deep CDL prediction model are replicated toconfigure the deep learning management zones prediction model, and theremaining 4 layers are trained on the historical unlabeled and labeledimagery data to classify learned features into management zones. Oneaspect of the deep learning management zones prediction model comprisesdetermining polygons within a sequence of image tiles where adjacentpixels in the annotated imagery data are labeled as being part of amanagement zone.

Once trained, the deep management zones prediction model may be executedusing parameters derived from the training along with current yearimagery data to generate predicted management zones for any parcel inthe continental United States. Since imagery tiles and correspondingpredicted management zones are only associated with longitude andlatitude, the present invention contemplates stitching adjacentmanagement zones tiles together using coordinates of farms and parcelsas stored in the parcel database 221 to provide for a field-levellayover of management zones. This aggregation of management zonepredictions at the field level is performed by the database managementprocessor 233.

Like the deep CDL prediction model, the deep management zones predictionmodel employs 128×128 pixel historical imagery tiles, where each pixelcorresponds to 10-meter ground resolution for Sentinel images. Bothlabeled and unlabeled versions of each tile are employed for training.Each tile comprises the five above noted optical and infrared bands(i.e., red, green, blue, near infrared, and cloud mask) that areaggregated into an enhanced vegetative index as described above.Preferably, ten tiles are generated per month, equally spaced in time,for the months of May through October, thus providing 60 tiles per yearfor both training and execution. Other numbers of tiles per year arecontemplated. Accordingly, each labeled and unlabeled tile comprises a1,280 meter by 1,280 meter geographic region and these tiles are used astraining inputs, as will be described below, for predictions ofmanagement zones that match the labeled imagery tiles. In oneembodiment, the previous 2 years of labeled and unlabeled imagery dataare used to train the model.

Operationally, step 616 accesses the deep CDL prediction modelparameters determined in the flow of FIG. 4 from database 612 and,preferably, the early feature determination layer parameters areextracted and applied to configure the early layers of the deepmanagement zones prediction model.

Database 614 provides unannotated and corresponding annotated imagesequences randomly to train the upper layers (“later layers”) of themodel at block 610. Backward propagation of is employed to train theconvolutional deep learning network via bus BACKPROPAGATION. Once thedeep management zones prediction model is trained at step 610,parameters for the 12-layer deep learning model are provided via busDMZP and stored in a deep management zones prediction model database618. The present invention contemplates retraining the deep managementzones prediction model as management practices are modified or added.

Referring to FIG. 7 , a flow diagram 700 is presented depicting a methodaccording to the present invention for executing a deep management zonesmodel, such as may be performed by the system 200 of FIG. 2 . Oncetrained in accordance with the steps discussed above with reference toFIG. 6 , the deep management zones prediction model is configured usingthe model parameters derived during training. These model parameters areprovided from database 618 on bus DMZP. Current year imagery data forthe growing season is accessed from database 704 on bus CI and iscleansed and formatted in step 708 in the same manner as is discussedabove with reference to step 408 of FIG. 4 . Preferably, the currentyear imagery data is of the same format (128×128) as used for trainingand comprises 60 tiles that are distributed approximately even in timeover a 6-month growing season from May through October. Other numbers oftiles are contemplated to comport with the same number of tiles used fortraining as discussed with reference to FIG. 6 . For example, if asequence of 12 labeled and unlabeled tiles per growing season are usedto train the deep management zones prediction model, then 12 tiles ofcurrent imagery data are employed for execution. While the deepmanagement zones prediction model is trained to predict management zonesfor the entire continental United States, the present inventors notethat selected geographical areas may be addressed through execution ofthe model, where the areas are demarcated by the imagery data that isprovided via bus FCI.

The deep management zones prediction model is sequentially executed atstep 710 to generate predicted management zones that correspond to eachof the image sequences provided via bus FCI. The predicted managementzones, as demarcated by polygons within the image sequences, is providedvia bus PMZ and is stored in database 723.

It is noted that geographic range of predicted management zones that isgenerated by the deep management zones prediction model at step 710 is afunction of the geographic range of current imagery data provided to themodel via bus FCI. For example, a user may be interested in managementzones for a state (e.g., Kentucky) or a growing region (e.g., the cornbelt). Accordingly, only current imagery covering the desired geographicarea is retrieved from database 704. Since imagery tiles andcorresponding predicted management zones are only associated withlongitude and latitude, the present invention contemplates stitchingadjacent management zones tiles together using coordinates of farms andparcels as stored in the parcel database 221 to provide for afield-level layover of management zones, thus providing detail aboutmanagement practices employed on individual farms. This aggregation ofmanagement zones predictions at the field level is performed by thedatabase management processor 233.

The present inventors note that virtually any agricultural managementpractice could conceivably be determined using parameters of earlylayers of the deep CDL prediction model in transfer learning cases, ifsufficient ground truth data existed. Indeed, it is noted that thepresent invention comprehends transfer learning techniques as discussedabove to configure models for detection of crop rotation, covercropping, irrigation, and tillage practices based solely on processingof current satellite imagery data by models trained to detect thesepractices using sufficient ground truth data. However, presently thereis insufficient commercially available ground truth data to determinethese practices via application of transfer learning to deep neuralnetworks. Accordingly, the present invention also contemplatesinferences of these practices using statistical inference techniques, aswill now be discussed with reference to FIG. 8 .

Finally turning to FIG. 8 , a flow diagram 800 is presented featuring amethod according to the present invention for inferring implementationof agricultural management practices within parcel management zones.Using data from a parcel database 821, management practices database805, predicted CDL database 822, predicted management zones database823, weather database 804, and current imagery database 802, a cropinference processor 835 aggregates current imagery data into enhancedvegetative indices for given parcels and processes the aggregatedenhanced vegetative index (EVI) data against EVI maturity curvesprovided by the management practices database 805 to determine croptype, planting date, harvest date, and maturity. The crop inferenceprocessor 835 is configured to infer what crop is growing in aparticular management zone within a particular field, and when that cropemerged from the ground. This is done by monitoring the vegetative indexover time. Different crops have different vegetative index curves, so byobserving the increases in EVI over time, the crop inference processor835 can infer what crop is growing and when the crop was planted.Results of these inferences are stored along with other field andmanagement zone data in the parcel database 821 and are provided on busCROP. Though EVI is preferably employed, the present invention alsocontemplates use of Normalized Difference Vegetative Index (NDVI)images.

Using data from a parcel database 821, management practices database805, predicted CDL database 822, predicted management zones database823, weather database 804, and current imagery database 802, anirrigation inference processor 836 aggregates current imagery data intovegetative indices for given parcels and processes the vegetative indexdata against maturity curves along with rainfall data obtained from theweather database 804, and to thus to infer the type of irrigationpractices (i.e., conventional irrigation, low irrigation, no irrigation)applied to each management zone. Results of these inferences are storedalong with other field and management zone data in the parcel database821 and are provided on bus IRR.

Using data from a parcel database 821, management practices database805, predicted CDL database 822, predicted management zones database823, weather database 804, and current imagery database 802, a tillageinference processor 837 distinguishes different tillage types as afunction of the amount of residue present on a management zone within afield for, as one skilled in the art will appreciate, different amountsof residue present on the field result in different near-IR signatures.Accordingly, the tillage inference processor 837 may employ one or moreresidue indices such as, but not limited to, Normalized DifferenceTillage Index (NDTI), Shortwave Infrared Normalized Difference ResidueIndex (SINDRI), and Cellulose Absorption Index (CAI) to distinguishtillage practices (i.e., conventional tillage, conservation tillage, notillage) in a manner substantially similar to the employment of EVI andother spectral indices to determine crop type and maturity. Results ofthese inferences are stored along with other field and management zonedata in the parcel database 821 and are provided on bus TILL.

Portions of the present invention and corresponding detailed descriptionare presented in terms of software or algorithms, and symbolicrepresentations of operations on data bits within a computer memory.These descriptions and representations are the ones by which those ofordinary skill in the art effectively convey the substance of their workto others of ordinary skill in the art. An algorithm, as the term isused here, and as it is used generally, is conceived to be aself-consistent sequence of steps leading to a desired result. The stepsare those requiring physical manipulations of physical quantities.Usually, though not necessarily, these quantities take the form ofoptical, electrical, or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise, or as is apparent from the discussion,terms such as “processing” or “computing” or “calculating” or“determining” or “displaying” or the like, refer to the action andprocesses of a computer system, a microprocessor, a central processingunit, or similar electronic computing device, that manipulates andtransforms data represented as physical, electronic quantities withinthe computer system's registers and memories into other data similarlyrepresented as physical quantities within the computer system memoriesor registers or other such information storage, transmission or displaydevices.

Note also that the software implemented aspects of the invention aretypically encoded on some form of program storage medium or implementedover some type of transmission medium. The program storage medium may beelectronic (e.g., read only memory, flash read only memory, electricallyprogrammable read only memory), random access memory magnetic (e.g., afloppy disk or a hard drive) or optical (e.g., a compact disk read onlymemory, or “CD ROM”), and may be read only or random access. Similarly,the transmission medium may be metal traces, twisted wire pairs, coaxialcable, optical fiber, or some other suitable transmission medium knownto the art. The invention is not limited by these aspects of any givenimplementation.

The particular embodiments disclosed above are illustrative only, andthose skilled in the art will appreciate that they can readily use thedisclosed conception and specific embodiments as a basis for designingor modifying other structures for carrying out the same purposes of thepresent invention, and that various changes, substitutions andalterations can be made herein without departing from the scope of theinvention as set forth by the appended claims. For example,components/elements of the systems and/or apparatuses may be integratedor separated. In addition, the operation of the systems and apparatusesdisclosed herein may be performed by more, fewer, or other componentsand the methods described may include more, fewer, or other steps.Additionally, unless otherwise specified steps may be performed in anysuitable order.

Although specific advantages have been enumerated above, variousembodiments may include some, none, or all of the enumerated advantages.

What is claimed is:
 1. A computer-implemented method for determiningagricultural tillage management practices for use within a currentgrowing year, the computer-implemented method comprising: retrieving afirst set of records from a historical cropland data layer database,wherein the first set of records corresponds to randomly sampled areasof a first geographic region taken over a first time period for a firstnumber of years; retrieving a second set of records from a historicalimagery database, wherein the second set of records corresponds to therandomly sampled areas of the first geographic region, the first timeperiod, and the first number of years; employing the second set ofrecords as inputs to train a first deep learning convolutional neuralnetwork to generate the first set of records and using parametersgenerated during training to configure a trained first deep learningconvolutional neural network for execution; configuring a second deeplearning convolutional neural network using parameters corresponding toearly layers of the trained first deep learning convolutional neuralnetwork; retrieving a third set of records and a fourth set of recordsfrom an annotated imagery database, wherein the third set of recordscomprises unannotated image versions corresponding to a secondgeographic region, and wherein the fourth set of records comprisesannotated image versions corresponding to the second geographic region,and wherein the annotated image versions comprise annotations indicativeof management zones, and wherein the third and fourth sets of recordscorrespond to a second time period for a second number of years;employing the third set of records as inputs to train upper layers ofthe second deep learning convolutional neural network to generate thefourth set of records and using parameters generated during training toconfigure a trained second deep learning convolutional neural networkfor execution; retrieving a fifth set of records from a current imagerydatabase, wherein the fifth set of records comprises corresponds to athird geographic region, and wherein the fifth set of recordscorresponds to the second time period and the current growing year;using the fifth set of records as inputs and executing the trainedsecond deep learning convolutional neural network to generate predictedagricultural management zones for the current growing year; andaggregating the fifth set of record into residue indices for parcelswithin the third geographic region, and processing the residue indicesover the second time period for the current growing year to infertillage practices for each of the predicted agricultural managementzones as demarcated by boundaries of each of the parcels.
 2. Thecomputer-implemented method as recited in claim 1, wherein the trainedfirst deep learning convolutional neural network and the trained seconddeep learning convolutional neural network each comprise 5 layers. 3.The computer-implemented method as recited in claim 2, wherein thesecond deep learning convolutional neural network uses parameterscorresponding to early layers of the trained first deep learningconvolutional neural network.
 4. The computer-implemented method asrecited in claim 1, wherein each of the second, third, fourth, and fifthsets of records each comprise 128×128 pixel images.
 5. Thecomputer-implemented method as recited in claim 4, wherein the each ofthe 128×128 pixel images comprise Sentinel satellite red channel, bluechannel, green channel, near infrared channel, and cloud mask channel.6. The computer-implemented method as recited in claim 4, wherein theprescribed time period comprises May through October, and wherein thenumber of 128×128 pixel images for each of the second number of yearscomprises 60 images.
 7. The computer-implemented method as recited inclaim 1, wherein the second number of years comprises three 2 yearsprevious to the current growing year.
 8. A computer-readable storagemedium storing instructions that, when executed by a computer, cause thecomputer to perform a method for determining agricultural tillagemanagement practices for use within a current growing year, the methodcomprising: retrieving a first set of records from a historical croplanddata layer database, wherein the first set of records corresponds torandomly sampled areas of a first geographic region taken over a firsttime period for a first number of years; retrieving a second set ofrecords from a historical imagery database, wherein the second set ofrecords corresponds to the randomly sampled areas of the firstgeographic region, the first time period, and the first number of years;employing the second set of records as inputs to train a first deeplearning convolutional neural network to generate the first set ofrecords and using parameters generated during training to configure atrained first deep learning convolutional neural network for execution;configuring a second deep learning convolutional neural network usingparameters corresponding to early layers of the trained first deeplearning convolutional neural network; retrieving a third set of recordsand a fourth set of records from an annotated imagery database, whereinthe third set of records comprises unannotated image versionscorresponding to a second geographic region, and wherein the fourth setof records comprises annotated image versions corresponding to thesecond geographic region, and wherein the annotated image versionscomprise annotations indicative of management zones, and wherein thethird and fourth sets of records correspond to a second time period fora second number of years; employing the third set of records as inputsto train upper layers of the second deep learning convolutional neuralnetwork to generate the fourth set of records and using parametersgenerated during training to configure a trained second deep learningconvolutional neural network for execution; retrieving a fifth set ofrecords from a current imagery database, wherein the fifth set ofrecords comprises corresponds to a third geographic region, and whereinthe fifth set of records corresponds to the second time period and thecurrent growing year; using the fifth set of records as inputs andexecuting the trained second deep learning convolutional neural networkto generate predicted agricultural management zones for the currentgrowing year; and aggregating the fifth set of record into residueindices for parcels within the third geographic region, and processingthe residue indices over the second time period for the current growingyear to infer tillage practices for each of the predicted agriculturalmanagement zones as demarcated by boundaries of each of the parcels. 9.The computer-readable storage medium as recited in claim 8, wherein thetrained first deep learning convolutional neural network and the trainedsecond deep learning convolutional neural network each comprise 5layers.
 10. The computer-readable storage medium as recited in claim 9,wherein the second deep learning convolutional neural network usesparameters corresponding to early layers of the trained first deeplearning convolutional neural network.
 11. The computer-readable storagemedium as recited in claim 8, wherein each of the second, third, fourth,and fifth sets of records each comprise 128×128 pixel images.
 12. Thecomputer-readable storage medium as recited in claim 11, wherein theeach of the 128×128 pixel images comprise Sentinel satellite redchannel, blue channel, green channel, near infrared channel, and cloudmask channel.
 13. The computer-readable storage medium as recited inclaim 11, wherein the prescribed time period comprises May throughOctober, and wherein the number of 128×128 pixel images for each of thesecond number of years comprises 60 images.
 14. The computer-readablestorage medium as recited in claim 8, wherein the second number of yearscomprises three 2 years previous to the current growing year.
 15. Acomputer program product for determining agricultural tillage managementpractices for use within a current growing year, the computer programproduct comprising: a computer readable non-transitory medium havingcomputer readable program code stored thereon, the computer readableprogram code comprising: program instructions to retrieve a first set ofrecords from a historical cropland data layer database, wherein thefirst set of records corresponds to randomly sampled areas of a firstgeographic region taken over a first time period for a first number ofyears; program instructions to retrieve a second set of records from ahistorical imagery database, wherein the second set of recordscorresponds to the randomly sampled areas of the first geographicregion, the first time period, and the first number of years; programinstructions to employ the second set of records as inputs to train afirst deep learning convolutional neural network to generate the firstset of records and to use parameters generated during training toconfigure a trained first deep learning convolutional neural network forexecution; program instructions to configure a second deep learningconvolutional neural network using parameters corresponding to earlylayers of the trained first deep learning convolutional neural network;program instructions to retrieve a third set of records and a fourth setof records from an annotated imagery database, wherein the third set ofrecords comprises unannotated image versions corresponding to a secondgeographic region, and wherein the fourth set of records comprisesannotated image versions corresponding to the second geographic region,and wherein the annotated image versions comprise annotations indicativeof management zones, and wherein the third and fourth sets of recordscorrespond to a second time period for a second number of years; programinstructions to employ the third set of records as inputs to train upperlayers of the second deep learning convolutional neural network togenerate the fourth set of records and to use parameters generatedduring training to configure a trained second deep learningconvolutional neural network for execution; program instructions to usethe fifth set of records as inputs and to execute the trained seconddeep learning convolutional neural network to generate predictedagricultural management zones for the current growing year; and programinstructions to aggregate the fifth set of record into residue indicesfor parcels within the third geographic region, and processing theresidue indices over the second time period for the current growing yearto infer tillage practices for each of the predicted agriculturalmanagement zones as demarcated by boundaries of each of the parcels. 16.The computer program product as recited in claim 15, wherein the trainedfirst deep learning convolutional neural network and the trained seconddeep learning convolutional neural network each comprise 5 layers. 17.The computer program product as recited in claim 16, wherein the seconddeep learning convolutional neural network uses parameters correspondingto early layers of the trained first deep learning convolutional neuralnetwork.
 18. The computer program product as recited in claim 15,wherein each of the second, third, fourth, and fifth sets of recordseach comprise 128×128 pixel images.
 19. The computer program product asrecited in claim 18, wherein the prescribed time period comprises Maythrough October, and wherein the number of 128×128 pixel images for eachof the second number of years comprises 60 images.
 20. The computerprogram product as recited in claim 15, wherein the second number ofyears comprises three 2 years previous to the current growing year.